关于相关天气和气候矮行星的性能可移植性的报告
Report on the performance portability demonstrated for the relevant Weather & Climate Dwarfs
摘要：
本文档是为ESCAPE项目创建的可交付成果报告之一。ESCAPE是Energy-efficient Scalable Algorithms for Weather Prediction at Exascale的缩写。该项目为欧洲业务数值天气预报和未来气候模型开发世界级的极端规模计算能力。这是通过识别天气和气候侏儒来完成的，这些侏儒是计算和通信方面的关键模式（本着伯克利侏儒的精神）。然后针对不同的硬件架构（单节点和多节点）优化这些侏儒，并探索替代算法。性能可移植性是通过使用领域特定的语言来解决的。此交付件提供了对ESCAPE中使用不同编程模型将不同的侏儒移植到加速器所执行的工作的评估。评估的一个关键指标是所产生的移植工作的性能可移植性。可移植性意味着包含数值运算符的单个源代码可以在多个体系结构中编译和运行，而性能可移植性还要求单个源代码在所有不同的体系结构中有效运行。作为D2.1等其他可交付成果的结果，D2.4 ESCAPE提供了一系列移植到不同计算架构（如传统CPU、Intel XeonPhi和NVIDIA GPU）的侏儒。此外，D3.3经过优化过程，以获得高效和节能的侏儒。在这个交付中，我们介绍了所采用的不同编程模型及其用于移植各种ESCAPE侏儒的回顾。不同的方法，如性能的可移植性，可读性的数值方法，努力端口的侏儒和效率的实现获得不同的指标的基础上的最终评价报告。
英文摘要：
This document is one of the deliverable reports created for the ESCAPE project. ESCAPE stands for Energy-efficient Scalable Algorithms for Weather Prediction at Exascale. The project develops world-class, extreme-scale computing capabilities for European operational numerical weather prediction and future climate models. This is done by identifying Weather & Climate dwarfs which are key patterns in terms of computation and communication (in the spirit of the Berkeley dwarfs). These dwarfs are then optimised for different hardware architectures (single and multi-node) and alternative algorithms are explored. Performance portability is addressed through the use of domain specific languages. This deliverable provides an evaluation of the work performed within ESCAPE to port different dwarfs to accelerators, using different programming models. A key metric of the evaluation is the performance portability of the resulting porting efforts. Portability means that a single source code containing the numerical operators can be compiled and run in multiple architectures, while performance portability additionally requires that the single source code runs efficiently in all the different architectures. As results of other deliverables like D2.1, D2.4 ESCAPE provides a collection of dwarfs ported to different computing architectures like traditional CPUs, Intel XeonPhi and NVIDIA GPUs. Additionally D3.3 went through an optimization process to obtain efficient and energy efficient dwarfs. In this deliverable we present a review of the different programming models employed and their use to port various dwarfs of ESCAPE. A final evaluation of the different approaches based on different metrics like performance portability, readability of the numerical methods, efforts to port a dwarf and efficiency of the implementation obtained is reported.
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